quantrs2-device 0.1.3

//! Quantum Error Correction Configuration Types
//!
//! This module defines all configuration types for the QEC system:
//! - Main QECConfig with all subsystem configurations
//! - QEC strategies and code types
//! - ML configuration for pattern recognition and prediction
//! - Monitoring and optimization configurations
//! - Internal structures for caching and statistics

use std::collections::{BTreeMap, HashMap, VecDeque};
use std::hash::Hasher;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, SystemTime};

use quantrs2_core::qubit::QubitId;
use scirs2_core::ndarray::{Array1, Array2};
use serde::{Deserialize, Serialize};

use crate::{
    calibration::CalibrationManager, prelude::SciRS2NoiseModeler, topology::HardwareTopology,
};

use super::{
    adaptive::{
        self, AccessControl, ActivationFunction, AlertSuppression, AlertingConfig,
        ArchitectureType, ArchivalStrategy, ConnectionPattern, ConsistencyLevel, DashboardConfig,
        DataAugmentationConfig, DataCollectionConfig, DataPreprocessingConfig, DataRetention,
        DataSource, DeploymentStrategy, DimensionalityReductionMethod, DriftDetection,
        EnvironmentConfig, EnvironmentType, EscalationRules, FeatureSelectionMethod,
        HardwareAcceleration, InferenceCaching, LayerConfig, LayerType, LossFunction,
        ModelDeployment, ModelManagementConfig, ModelMonitoring, ModelOptimization,
        ModelVersioning, MonitoringAlertingConfig, NormalizationMethod, OptimizerType,
        PerformanceMonitoring, ResourceAllocation, RollbackStrategy, ScalingConfig, StorageBackend,
        StorageConfig, VersionControlSystem,
    },
    codes::{QECCodeType, SurfaceCodeLayout},
    detection, mitigation,
    types::{CorrectionOperation, DeviceState, ExecutionContext, SyndromePattern},
};

pub struct QECConfig {
    /// QEC code type
    pub code_type: QECCodeType,
    /// Code distance
    pub distance: usize,
    /// QEC strategies
    pub strategies: Vec<QECStrategy>,
    /// Enable ML optimization
    pub enable_ml_optimization: bool,
    /// Enable adaptive thresholds
    pub enable_adaptive_thresholds: bool,
    /// Correction timeout
    pub correction_timeout: Duration,
    /// Syndrome detection configuration
    pub syndrome_detection: detection::SyndromeDetectionConfig,
    /// ML configuration
    pub ml_config: QECMLConfig,
    /// Adaptive configuration
    pub adaptive_config: adaptive::AdaptiveQECConfig,
    /// Monitoring configuration
    pub monitoring_config: QECMonitoringConfig,
    /// Optimization configuration
    pub optimization_config: QECOptimizationConfig,
    /// Error mitigation configuration
    pub error_mitigation: mitigation::ErrorMitigationConfig,
    /// Error correction codes to use
    pub error_codes: Vec<QECCodeType>,
    /// Error correction strategy
    pub correction_strategy: QECStrategy,
    /// Adaptive QEC configuration
    pub adaptive_qec: adaptive::AdaptiveQECConfig,
    /// Performance optimization
    pub performance_optimization: QECOptimizationConfig,
}

/// Error correction strategies
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum QECStrategy {
    /// Active error correction
    ActiveCorrection,
    /// Passive monitoring
    PassiveMonitoring,
    /// Adaptive threshold adjustment
    AdaptiveThreshold,
    /// ML-driven error correction
    MLDriven,
    /// Hybrid approach
    HybridApproach,
    /// Passive error correction
    Passive,
    /// Active error correction with periodic syndrome measurement
    ActivePeriodic { cycle_time: Duration },
    /// Adaptive error correction based on noise levels
    Adaptive,
    /// Fault-tolerant error correction
    FaultTolerant,
    /// Hybrid approach (legacy)
    Hybrid { strategies: Vec<Self> },
}

#[derive(Debug, Clone)]
pub struct ErrorCorrectionCycleResult {
    pub syndromes_detected: Option<Vec<SyndromePattern>>,
    pub corrections_applied: Option<Vec<CorrectionOperation>>,
    pub success: bool,
}

// Note: SyndromePattern already defined above, removing duplicate

// ExecutionContext and DeviceState are imported from types

/// Error statistics for adaptive learning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorStatistics {
    pub error_rates_by_type: HashMap<String, f64>,
    pub error_correlations: Array2<f64>,
    pub temporal_patterns: Vec<TemporalPattern>,
    pub spatial_patterns: Vec<SpatialPattern>,
    pub prediction_accuracy: f64,
    pub last_updated: SystemTime,
}

/// Temporal error patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalPattern {
    pub pattern_type: String,
    pub frequency: f64,
    pub amplitude: f64,
    pub phase: f64,
    pub confidence: f64,
}

/// Spatial error patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpatialPattern {
    pub pattern_type: String,
    pub affected_qubits: Vec<usize>,
    pub correlation_strength: f64,
    pub propagation_direction: Option<String>,
}

/// Adaptive thresholds for QEC
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptiveThresholds {
    pub error_detection_threshold: f64,
    pub correction_confidence_threshold: f64,
    pub syndrome_pattern_threshold: f64,
    pub ml_prediction_threshold: f64,
    pub adaptation_rate: f64,
    pub stability_window: Duration,
}

// AdaptiveThresholds already defined above

/// Machine learning model for error correction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLModel {
    pub model_type: String,
    pub model_data: Vec<u8>, // Serialized model
    pub training_accuracy: f64,
    pub validation_accuracy: f64,
    pub last_trained: SystemTime,
    pub feature_importance: HashMap<String, f64>,
}

/// Correction performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrectionMetrics {
    pub total_corrections: usize,
    pub successful_corrections: usize,
    pub false_positives: usize,
    pub false_negatives: usize,
    pub average_correction_time: Duration,
    pub resource_utilization: ResourceUtilization,
    pub fidelity_improvement: f64,
}

/// Resource utilization for QEC
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUtilization {
    pub auxiliary_qubits_used: f64,
    pub measurement_overhead: f64,
    pub classical_processing_time: f64,
    pub memory_usage: usize,
}

/// Cached optimization results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CachedOptimization {
    pub optimization_result: OptimizationResult,
    pub context_hash: u64,
    pub timestamp: SystemTime,
    pub hit_count: usize,
    pub performance_score: f64,
}

/// QEC optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationResult {
    pub optimal_strategy: QECStrategy,
    pub predicted_performance: f64,
    pub resource_requirements: ResourceRequirements,
    pub confidence_score: f64,
    pub optimization_time: Duration,
}

/// Resource requirements for QEC strategy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    pub auxiliary_qubits: usize,
    pub syndrome_measurements: usize,
    pub classical_processing: Duration,
    pub memory_mb: usize,
    pub power_watts: f64,
}

// Default implementations for test compatibility
impl Default for ErrorStatistics {
    fn default() -> Self {
        Self {
            error_rates_by_type: HashMap::new(),
            error_correlations: Array2::zeros((0, 0)),
            temporal_patterns: Vec::new(),
            spatial_patterns: Vec::new(),
            prediction_accuracy: 0.0,
            last_updated: SystemTime::now(),
        }
    }
}

impl Default for AdaptiveThresholds {
    fn default() -> Self {
        Self {
            error_detection_threshold: 0.95,
            correction_confidence_threshold: 0.90,
            syndrome_pattern_threshold: 0.85,
            ml_prediction_threshold: 0.80,
            adaptation_rate: 0.01,
            stability_window: Duration::from_secs(60),
        }
    }
}

impl Default for CorrectionMetrics {
    fn default() -> Self {
        Self {
            total_corrections: 0,
            successful_corrections: 0,
            false_positives: 0,
            false_negatives: 0,
            average_correction_time: Duration::from_millis(100),
            resource_utilization: ResourceUtilization::default(),
            fidelity_improvement: 0.0,
        }
    }
}

impl Default for ResourceUtilization {
    fn default() -> Self {
        Self {
            auxiliary_qubits_used: 0.0,
            measurement_overhead: 0.0,
            classical_processing_time: 0.0,
            memory_usage: 0,
        }
    }
}
impl Default for QECConfig {
    fn default() -> Self {
        Self {
            code_type: QECCodeType::SurfaceCode {
                distance: 5,
                layout: SurfaceCodeLayout::Square,
            },
            distance: 5,
            strategies: vec![QECStrategy::Adaptive],
            enable_ml_optimization: true,
            enable_adaptive_thresholds: true,
            correction_timeout: Duration::from_millis(100),
            adaptive_config: adaptive::AdaptiveQECConfig {
                enable_real_time_adaptation: true,
                adaptation_window: Duration::from_secs(60),
                performance_threshold: 0.95,
                enable_threshold_adaptation: true,
                enable_strategy_switching: true,
                learning_rate: 0.01,
                enable_adaptive: true,
                strategies: vec![],
                learning: adaptive::AdaptiveLearningConfig {
                    algorithms: vec![],
                    online_learning: adaptive::OnlineLearningConfig {
                        enable_online: true,
                        learning_rate_adaptation: adaptive::LearningRateAdaptation::Adaptive,
                        concept_drift: adaptive::ConceptDriftConfig {
                            enable_detection: false,
                            methods: vec![],
                            responses: vec![],
                        },
                        model_updates: adaptive::ModelUpdateConfig {
                            frequency: adaptive::UpdateFrequency::EventTriggered,
                            triggers: vec![],
                            strategies: vec![],
                        },
                    },
                    transfer_learning: adaptive::TransferLearningConfig {
                        enable_transfer: false,
                        source_domains: vec![],
                        strategies: vec![],
                        domain_adaptation: adaptive::DomainAdaptationConfig {
                            methods: vec![],
                            validation: vec![],
                        },
                    },
                    meta_learning: adaptive::MetaLearningConfig {
                        enable_meta: false,
                        algorithms: vec![],
                        task_distribution: adaptive::TaskDistributionConfig {
                            task_types: vec![],
                            complexity_range: (0.0, 1.0),
                            generation_strategy: adaptive::TaskGenerationStrategy::Random,
                        },
                        meta_optimization: adaptive::MetaOptimizationConfig {
                            optimizer: adaptive::MetaOptimizer::Adam,
                            learning_rates: adaptive::LearningRates {
                                inner_lr: 0.01,
                                outer_lr: 0.001,
                                adaptive: true,
                            },
                            regularization: adaptive::MetaRegularization {
                                regularization_type: adaptive::RegularizationType::L2,
                                strength: 0.001,
                            },
                        },
                    },
                },
                realtime_optimization: adaptive::RealtimeOptimizationConfig {
                    enable_realtime: true,
                    objectives: vec![],
                    algorithms: vec![],
                    constraints: adaptive::ResourceConstraints {
                        time_limit: Duration::from_millis(100),
                        memory_limit: 1024 * 1024,
                        power_budget: 100.0,
                        hardware_constraints: adaptive::HardwareConstraints {
                            connectivity: adaptive::ConnectivityConstraints {
                                coupling_map: vec![],
                                max_distance: 10,
                                routing_overhead: 1.2,
                            },
                            gate_fidelities: std::collections::HashMap::new(),
                            coherence_times: adaptive::CoherenceTimes {
                                t1_times: std::collections::HashMap::new(),
                                t2_times: std::collections::HashMap::new(),
                                gate_times: std::collections::HashMap::new(),
                            },
                        },
                    },
                },
                feedback_control: adaptive::FeedbackControlConfig {
                    enable_feedback: true,
                    algorithms: vec![],
                    sensors: adaptive::SensorConfig {
                        sensor_types: vec![],
                        sampling_rates: std::collections::HashMap::new(),
                        noise_characteristics: adaptive::NoiseCharacteristics {
                            gaussian_noise: 0.01,
                            systematic_bias: 0.0,
                            temporal_correlation: 0.1,
                        },
                    },
                    actuators: adaptive::ActuatorConfig {
                        actuator_types: vec![],
                        response_times: std::collections::HashMap::new(),
                        control_ranges: std::collections::HashMap::new(),
                    },
                },
                prediction: adaptive::PredictionConfig::default(),
                optimization: adaptive::OptimizationConfig::default(),
            },
            optimization_config: QECOptimizationConfig {
                enable_optimization: true,
                enable_code_optimization: true,
                enable_layout_optimization: true,
                enable_scheduling_optimization: true,
                optimization_algorithm:
                    crate::unified_benchmarking::config::OptimizationAlgorithm::GradientDescent,
                optimization_objectives: vec![],
                constraint_satisfaction: ConstraintSatisfactionConfig {
                    hardware_constraints: vec![],
                    resource_constraints: vec![],
                    performance_constraints: vec![],
                },
                targets: vec![],
                metrics: vec![],
                strategies: vec![],
            },
            error_codes: vec![QECCodeType::SurfaceCode {
                distance: 5,
                layout: SurfaceCodeLayout::Square,
            }],
            correction_strategy: QECStrategy::Adaptive,
            syndrome_detection: detection::SyndromeDetectionConfig {
                enable_parallel_detection: true,
                detection_rounds: 3,
                stabilizer_measurement_shots: 1000,
                enable_syndrome_validation: true,
                validation_threshold: 0.95,
                enable_error_correlation: true,
                enable_detection: true,
                detection_frequency: 1000.0,
                detection_methods: vec![],
                pattern_recognition: detection::PatternRecognitionConfig {
                    enable_recognition: true,
                    algorithms: vec![],
                    training_config: detection::PatternTrainingConfig {
                        training_size: 1000,
                        validation_split: 0.2,
                        epochs: 100,
                        learning_rate: 0.001,
                        batch_size: 32,
                    },
                    real_time_adaptation: false,
                },
                statistical_analysis: detection::SyndromeStatisticsConfig {
                    enable_statistics: true,
                    methods: vec![],
                    confidence_level: 0.95,
                    data_retention_days: 30,
                },
            },
            error_mitigation: mitigation::ErrorMitigationConfig {
                enable_zne: true,
                enable_symmetry_verification: true,
                enable_readout_correction: true,
                enable_dynamical_decoupling: true,
                mitigation_strategies: vec![],
                zne_config: mitigation::ZNEConfig {
                    noise_factors: vec![1.0, 1.5, 2.0],
                    extrapolation_method: mitigation::ExtrapolationMethod::Linear,
                    circuit_folding: mitigation::CircuitFoldingMethod::GlobalFolding,
                    enable_zne: true,
                    noise_scaling_factors: vec![1.0, 1.5, 2.0],
                    folding: mitigation::FoldingConfig {
                        folding_type: mitigation::FoldingType::Global,
                        global_folding: true,
                        local_folding: mitigation::LocalFoldingConfig {
                            regions: vec![],
                            selection_strategy: mitigation::RegionSelectionStrategy::Adaptive,
                            overlap_handling: mitigation::OverlapHandling::Ignore,
                        },
                        gate_specific: mitigation::GateSpecificFoldingConfig {
                            folding_rules: std::collections::HashMap::new(),
                            priority_ordering: vec![],
                            error_rate_weighting: false,
                            folding_strategies: std::collections::HashMap::new(),
                            default_strategy: mitigation::DefaultFoldingStrategy::Identity,
                            prioritized_gates: vec![],
                        },
                    },
                    richardson: mitigation::RichardsonConfig {
                        enable_richardson: false,
                        order: 2,
                        stability_check: true,
                        error_estimation: mitigation::ErrorEstimationConfig {
                            method: mitigation::ErrorEstimationMethod::Bootstrap,
                            bootstrap_samples: 100,
                            confidence_level: 0.95,
                        },
                    },
                },
                enable_mitigation: true,
                strategies: vec![],
                zne: mitigation::ZNEConfig {
                    noise_factors: vec![1.0, 1.5, 2.0],
                    extrapolation_method: mitigation::ExtrapolationMethod::Linear,
                    circuit_folding: mitigation::CircuitFoldingMethod::GlobalFolding,
                    enable_zne: true,
                    noise_scaling_factors: vec![1.0, 1.5, 2.0],
                    folding: mitigation::FoldingConfig {
                        folding_type: mitigation::FoldingType::Global,
                        global_folding: true,
                        local_folding: mitigation::LocalFoldingConfig {
                            regions: vec![],
                            selection_strategy: mitigation::RegionSelectionStrategy::Adaptive,
                            overlap_handling: mitigation::OverlapHandling::Ignore,
                        },
                        gate_specific: mitigation::GateSpecificFoldingConfig {
                            folding_rules: std::collections::HashMap::new(),
                            priority_ordering: vec![],
                            error_rate_weighting: false,
                            folding_strategies: std::collections::HashMap::new(),
                            default_strategy: mitigation::DefaultFoldingStrategy::Identity,
                            prioritized_gates: vec![],
                        },
                    },
                    richardson: mitigation::RichardsonConfig {
                        enable_richardson: false,
                        order: 2,
                        stability_check: true,
                        error_estimation: mitigation::ErrorEstimationConfig {
                            method: mitigation::ErrorEstimationMethod::Bootstrap,
                            bootstrap_samples: 100,
                            confidence_level: 0.95,
                        },
                    },
                },
                readout_mitigation: mitigation::ReadoutMitigationConfig {
                    enable_mitigation: true,
                    methods: vec![],
                    calibration: mitigation::ReadoutCalibrationConfig {
                        frequency: mitigation::CalibrationFrequency::Periodic(
                            std::time::Duration::from_secs(3600),
                        ),
                        states: vec![],
                        quality_metrics: vec![],
                    },
                    matrix_inversion: mitigation::MatrixInversionConfig {
                        method: mitigation::InversionMethod::PseudoInverse,
                        regularization: mitigation::RegularizationConfig {
                            regularization_type: mitigation::RegularizationType::L2,
                            parameter: 0.001,
                            adaptive: false,
                        },
                        stability: mitigation::NumericalStabilityConfig {
                            condition_threshold: 1e-12,
                            pivoting: mitigation::PivotingStrategy::Partial,
                            scaling: true,
                        },
                    },
                    tensored_mitigation: mitigation::TensoredMitigationConfig {
                        groups: vec![],
                        group_strategy: mitigation::GroupFormationStrategy::Topology,
                        crosstalk_handling: mitigation::CrosstalkHandling::Ignore,
                    },
                },
                gate_mitigation: mitigation::GateMitigationConfig {
                    enable_mitigation: true,
                    gate_configs: std::collections::HashMap::new(),
                    twirling: mitigation::TwirlingConfig {
                        enable_twirling: true,
                        twirling_type: mitigation::TwirlingType::Pauli,
                        groups: vec![],
                        randomization: mitigation::RandomizationStrategy::FullRandomization,
                    },
                    randomized_compiling: mitigation::RandomizedCompilingConfig {
                        enable_rc: true,
                        strategies: vec![],
                        replacement_rules: std::collections::HashMap::new(),
                        randomization_level: mitigation::RandomizationLevel::Medium,
                    },
                },
                symmetry_verification: mitigation::SymmetryVerificationConfig {
                    enable_verification: true,
                    symmetry_types: vec![],
                    protocols: vec![],
                    tolerance: mitigation::ToleranceSettings {
                        symmetry_tolerance: 0.01,
                        statistical_tolerance: 0.05,
                        confidence_level: 0.95,
                    },
                },
                virtual_distillation: mitigation::VirtualDistillationConfig {
                    enable_distillation: true,
                    protocols: vec![],
                    resources: mitigation::ResourceRequirements {
                        auxiliary_qubits: 2,
                        measurement_rounds: 3,
                        classical_processing: mitigation::ProcessingRequirements {
                            memory_mb: 1024,
                            computation_time: std::time::Duration::from_millis(100),
                            parallel_processing: false,
                        },
                    },
                    quality_metrics: vec![],
                },
            },
            adaptive_qec: adaptive::AdaptiveQECConfig {
                enable_real_time_adaptation: true,
                adaptation_window: Duration::from_secs(60),
                performance_threshold: 0.95,
                enable_threshold_adaptation: true,
                enable_strategy_switching: true,
                learning_rate: 0.01,
                enable_adaptive: true,
                strategies: vec![],
                learning: adaptive::AdaptiveLearningConfig {
                    algorithms: vec![],
                    online_learning: adaptive::OnlineLearningConfig {
                        enable_online: true,
                        learning_rate_adaptation: adaptive::LearningRateAdaptation::Adaptive,
                        concept_drift: adaptive::ConceptDriftConfig {
                            enable_detection: false,
                            methods: vec![],
                            responses: vec![],
                        },
                        model_updates: adaptive::ModelUpdateConfig {
                            frequency: adaptive::UpdateFrequency::EventTriggered,
                            triggers: vec![],
                            strategies: vec![],
                        },
                    },
                    transfer_learning: adaptive::TransferLearningConfig {
                        enable_transfer: false,
                        source_domains: vec![],
                        strategies: vec![],
                        domain_adaptation: adaptive::DomainAdaptationConfig {
                            methods: vec![],
                            validation: vec![],
                        },
                    },
                    meta_learning: adaptive::MetaLearningConfig {
                        enable_meta: false,
                        algorithms: vec![],
                        task_distribution: adaptive::TaskDistributionConfig {
                            task_types: vec![],
                            complexity_range: (0.0, 1.0),
                            generation_strategy: adaptive::TaskGenerationStrategy::Random,
                        },
                        meta_optimization: adaptive::MetaOptimizationConfig {
                            optimizer: adaptive::MetaOptimizer::Adam,
                            learning_rates: adaptive::LearningRates {
                                inner_lr: 0.01,
                                outer_lr: 0.001,
                                adaptive: true,
                            },
                            regularization: adaptive::MetaRegularization {
                                regularization_type: adaptive::RegularizationType::L2,
                                strength: 0.001,
                            },
                        },
                    },
                },
                realtime_optimization: adaptive::RealtimeOptimizationConfig {
                    enable_realtime: true,
                    objectives: vec![],
                    algorithms: vec![],
                    constraints: adaptive::ResourceConstraints {
                        time_limit: std::time::Duration::from_millis(100),
                        memory_limit: 1024 * 1024,
                        power_budget: 100.0,
                        hardware_constraints: adaptive::HardwareConstraints {
                            connectivity: adaptive::ConnectivityConstraints {
                                coupling_map: vec![],
                                max_distance: 10,
                                routing_overhead: 1.2,
                            },
                            gate_fidelities: std::collections::HashMap::new(),
                            coherence_times: adaptive::CoherenceTimes {
                                t1_times: std::collections::HashMap::new(),
                                t2_times: std::collections::HashMap::new(),
                                gate_times: std::collections::HashMap::new(),
                            },
                        },
                    },
                },
                feedback_control: adaptive::FeedbackControlConfig {
                    enable_feedback: true,
                    algorithms: vec![],
                    sensors: adaptive::SensorConfig {
                        sensor_types: vec![],
                        sampling_rates: std::collections::HashMap::new(),
                        noise_characteristics: adaptive::NoiseCharacteristics {
                            gaussian_noise: 0.01,
                            systematic_bias: 0.0,
                            temporal_correlation: 0.1,
                        },
                    },
                    actuators: adaptive::ActuatorConfig {
                        actuator_types: vec![],
                        response_times: std::collections::HashMap::new(),
                        control_ranges: std::collections::HashMap::new(),
                    },
                },
                prediction: adaptive::PredictionConfig::default(),
                optimization: adaptive::OptimizationConfig::default(),
            },
            performance_optimization: QECOptimizationConfig {
                enable_optimization: true,
                enable_code_optimization: true,
                enable_layout_optimization: true,
                enable_scheduling_optimization: true,
                optimization_algorithm:
                    crate::unified_benchmarking::config::OptimizationAlgorithm::GradientDescent,
                optimization_objectives: vec![],
                constraint_satisfaction: ConstraintSatisfactionConfig {
                    hardware_constraints: vec![],
                    resource_constraints: vec![],
                    performance_constraints: vec![],
                },
                targets: vec![],
                metrics: vec![],
                strategies: vec![],
            },
            ml_config: QECMLConfig {
                model_type: crate::unified_benchmarking::config::MLModelType::NeuralNetwork,
                training_data_size: 10000,
                validation_split: 0.2,
                enable_online_learning: true,
                feature_extraction: crate::ml_optimization::FeatureExtractionConfig {
                    enable_syndrome_history: true,
                    history_length: 100,
                    enable_spatial_features: true,
                    enable_temporal_features: true,
                    enable_correlation_features: true,
                    enable_auto_extraction: true,
                    circuit_features: crate::ml_optimization::CircuitFeatureConfig {
                        basic_properties: true,
                        gate_distributions: true,
                        depth_analysis: true,
                        connectivity_patterns: true,
                        entanglement_measures: false,
                        symmetry_analysis: false,
                        critical_path_analysis: false,
                    },
                    hardware_features: crate::ml_optimization::HardwareFeatureConfig {
                        topology_features: true,
                        calibration_features: true,
                        error_rate_features: true,
                        timing_features: false,
                        resource_features: false,
                        environmental_features: false,
                    },
                    temporal_features: crate::ml_optimization::TemporalFeatureConfig {
                        time_series_analysis: true,
                        trend_detection: true,
                        seasonality_analysis: false,
                        autocorrelation_features: false,
                        fourier_features: false,
                    },
                    statistical_features: crate::ml_optimization::StatisticalFeatureConfig {
                        moment_features: true,
                        distribution_fitting: false,
                        correlation_features: true,
                        outlier_features: false,
                        normality_tests: false,
                    },
                    graph_features: crate::ml_optimization::GraphFeatureConfig {
                        centrality_measures: false,
                        community_features: false,
                        spectral_features: false,
                        path_features: false,
                        clustering_features: false,
                    },
                    feature_selection: crate::ml_optimization::FeatureSelectionConfig {
                        enable_selection: true,
                        selection_methods: vec![
                            crate::ml_optimization::FeatureSelectionMethod::VarianceThreshold,
                        ],
                        num_features: Some(50),
                        selection_threshold: 0.01,
                    },
                    dimensionality_reduction:
                        crate::ml_optimization::DimensionalityReductionConfig {
                            enable_reduction: false,
                            reduction_methods: vec![],
                            target_dimensions: None,
                            variance_threshold: 0.95,
                        },
                },
                model_update_frequency: Duration::from_secs(3600),
                enable_ml: true,
                models: vec![],
                training: MLTrainingConfig {
                    batch_size: 32,
                    learning_rate: 0.001,
                    epochs: 100,
                    optimization_algorithm: "adam".to_string(),
                    data: TrainingDataConfig {
                        sources: vec![],
                        preprocessing: DataPreprocessingConfig {
                            normalization: NormalizationMethod::ZScore,
                            feature_selection: FeatureSelectionMethod::Statistical,
                            dimensionality_reduction: DimensionalityReductionMethod::PCA,
                        },
                        augmentation: DataAugmentationConfig {
                            enable: false,
                            techniques: vec![],
                            ratio: 1.0,
                        },
                    },
                    architecture: ModelArchitectureConfig {
                        architecture_type: ArchitectureType::Sequential,
                        layers: vec![LayerConfig {
                            layer_type: LayerType::Dense,
                            parameters: [("neurons".to_string(), 128.0)].iter().cloned().collect(),
                            activation: ActivationFunction::ReLU,
                        }],
                        connections: ConnectionPattern::FullyConnected,
                    },
                    parameters: TrainingParameters {
                        optimizer: OptimizerType::Adam,
                        loss_function: LossFunction::MeanSquaredError,
                        regularization_strength: 0.01,
                        learning_rate: 0.001,
                        batch_size: 32,
                        epochs: 100,
                    },
                    validation: adaptive::ValidationConfig {
                        method: adaptive::ValidationMethod::HoldOut,
                        split: 0.2,
                        cv_folds: 5,
                    },
                },
                inference: MLInferenceConfig {
                    mode: InferenceMode::Synchronous,
                    batch_processing: BatchProcessingConfig {
                        enable: false,
                        batch_size: 32,
                        timeout: std::time::Duration::from_secs(30),
                    },
                    timeout: std::time::Duration::from_secs(30),
                    caching: CachingConfig {
                        enable: true,
                        cache_size: 512,
                        ttl: std::time::Duration::from_secs(3600),
                        eviction_policy: adaptive::CacheEvictionPolicy::LRU,
                    },
                    optimization: InferenceOptimizationConfig {
                        enable_optimization: true,
                        optimization_strategies: vec!["model_pruning".to_string()],
                        performance_targets: vec!["latency".to_string()],
                        model_optimization: ModelOptimization::None,
                        hardware_acceleration: HardwareAcceleration::CPU,
                        caching: InferenceCaching {
                            enable: false,
                            cache_size: 1000,
                            eviction_policy: adaptive::CacheEvictionPolicy::LRU,
                        },
                    },
                },
                model_management: ModelManagementConfig {
                    versioning: ModelVersioning {
                        enable: false,
                        version_control: VersionControlSystem::Git,
                        rollback: RollbackStrategy::Manual,
                    },
                    deployment: ModelDeployment {
                        strategy: DeploymentStrategy::BlueGreen,
                        environment: EnvironmentConfig {
                            environment_type: EnvironmentType::Development,
                            resources: ResourceAllocation {
                                cpu: 1.0,
                                memory: 1024,
                                gpu: None,
                            },
                            dependencies: vec![],
                        },
                        scaling: ScalingConfig {
                            auto_scaling: false,
                            min_replicas: 1,
                            max_replicas: 3,
                            metrics: vec![],
                        },
                    },
                    monitoring: ModelMonitoring {
                        performance: PerformanceMonitoring {
                            metrics: vec![],
                            frequency: std::time::Duration::from_secs(60),
                            baseline_comparison: false,
                        },
                        drift_detection: DriftDetection {
                            enable: false,
                            methods: vec![],
                            sensitivity: 0.05,
                        },
                        alerting: AlertingConfig {
                            channels: vec![],
                            thresholds: std::collections::HashMap::new(),
                            escalation: EscalationRules {
                                levels: vec![],
                                timeouts: std::collections::HashMap::new(),
                            },
                        },
                    },
                },
                optimization: create_stub_ml_optimization_config(),
                validation: create_default_validation_config(),
            },
            monitoring_config: QECMonitoringConfig {
                enable_performance_tracking: true,
                enable_error_analysis: true,
                enable_resource_monitoring: true,
                reporting_interval: Duration::from_secs(60),
                enable_predictive_analytics: false,
                enable_monitoring: true,
                targets: vec![],
                dashboard: DashboardConfig {
                    enable: true,
                    components: vec![],
                    update_frequency: std::time::Duration::from_secs(5),
                    access_control: AccessControl {
                        authentication: false,
                        roles: vec![],
                        permissions: std::collections::HashMap::new(),
                    },
                },
                data_collection: DataCollectionConfig {
                    frequency: std::time::Duration::from_secs(1),
                    retention: DataRetention {
                        period: std::time::Duration::from_secs(3600 * 24 * 30),
                        archival: ArchivalStrategy::CloudStorage,
                        compression: false,
                    },
                    storage: StorageConfig {
                        backend: StorageBackend::FileSystem,
                        replication: 1,
                        consistency: ConsistencyLevel::Eventual,
                    },
                },
                alerting: MonitoringAlertingConfig {
                    rules: vec![],
                    channels: vec![],
                    suppression: AlertSuppression {
                        enable: false,
                        rules: vec![],
                        default_time: std::time::Duration::from_secs(300),
                    },
                },
            },
        }
    }
}

// Additional configuration types needed for test compatibility

// SyndromeDetectionConfig is now defined in detection module

/// Training data configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingDataConfig {
    pub sources: Vec<DataSource>,
    pub preprocessing: DataPreprocessingConfig,
    pub augmentation: DataAugmentationConfig,
}

/// Model architecture configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelArchitectureConfig {
    pub architecture_type: ArchitectureType,
    pub layers: Vec<LayerConfig>,
    pub connections: ConnectionPattern,
}

/// Training parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingParameters {
    pub optimizer: adaptive::OptimizerType,
    pub loss_function: adaptive::LossFunction,
    pub regularization_strength: f64,
    pub learning_rate: f64,
    pub batch_size: usize,
    pub epochs: usize,
}

/// ML Training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLTrainingConfig {
    pub batch_size: usize,
    pub learning_rate: f64,
    pub epochs: usize,
    pub optimization_algorithm: String,
    pub data: TrainingDataConfig,
    pub architecture: ModelArchitectureConfig,
    pub parameters: TrainingParameters,
    pub validation: adaptive::ValidationConfig,
}

/// ML Inference configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLInferenceConfig {
    pub mode: InferenceMode,
    pub batch_processing: BatchProcessingConfig,
    pub timeout: Duration,
    pub caching: CachingConfig,
    pub optimization: InferenceOptimizationConfig,
}

/// Inference modes
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum InferenceMode {
    Synchronous,
    Asynchronous,
    Streaming,
}

/// Batch processing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BatchProcessingConfig {
    pub enable: bool,
    pub batch_size: usize,
    pub timeout: Duration,
}

/// Caching configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CachingConfig {
    pub enable: bool,
    pub cache_size: usize,
    pub ttl: Duration,
    pub eviction_policy: adaptive::CacheEvictionPolicy,
}

/// Inference optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InferenceOptimizationConfig {
    pub enable_optimization: bool,
    pub optimization_strategies: Vec<String>,
    pub performance_targets: Vec<String>,
    pub model_optimization: adaptive::ModelOptimization,
    pub hardware_acceleration: adaptive::HardwareAcceleration,
    pub caching: adaptive::InferenceCaching,
}

/// QEC ML configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QECMLConfig {
    pub model_type: crate::unified_benchmarking::config::MLModelType,
    pub training_data_size: usize,
    pub validation_split: f64,
    pub enable_online_learning: bool,
    pub feature_extraction: crate::ml_optimization::FeatureExtractionConfig,
    pub model_update_frequency: Duration,
    // Additional fields for full compatibility
    pub enable_ml: bool,
    pub inference: MLInferenceConfig,
    pub model_management: adaptive::ModelManagementConfig,
    pub optimization: crate::ml_optimization::MLOptimizationConfig,
    pub validation: crate::ml_optimization::ValidationConfig,
    pub models: Vec<String>,
    pub training: MLTrainingConfig,
}

/// QEC monitoring configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QECMonitoringConfig {
    pub enable_performance_tracking: bool,
    pub enable_error_analysis: bool,
    pub enable_resource_monitoring: bool,
    pub reporting_interval: Duration,
    pub enable_predictive_analytics: bool,
    // Additional fields already defined in the complex struct above
    pub enable_monitoring: bool,
    pub targets: Vec<String>,
    pub dashboard: DashboardConfig,
    pub data_collection: DataCollectionConfig,
    pub alerting: MonitoringAlertingConfig,
}

/// QEC optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QECOptimizationConfig {
    pub enable_code_optimization: bool,
    pub enable_layout_optimization: bool,
    pub enable_scheduling_optimization: bool,
    pub optimization_algorithm: crate::unified_benchmarking::config::OptimizationAlgorithm,
    pub optimization_objectives: Vec<OptimizationObjective>,
    pub constraint_satisfaction: ConstraintSatisfactionConfig,
    pub enable_optimization: bool,
    pub targets: Vec<String>,
    pub metrics: Vec<String>,
    pub strategies: Vec<String>,
}

/// Optimization objectives for QEC
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationObjective {
    MaximizeLogicalFidelity,
    MinimizeOverhead,
    MinimizeLatency,
    MinimizeResourceUsage,
    MaximizeThroughput,
}

/// Constraint satisfaction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConstraintSatisfactionConfig {
    pub hardware_constraints: Vec<HardwareConstraint>,
    pub resource_constraints: Vec<ResourceConstraint>,
    pub performance_constraints: Vec<PerformanceConstraint>,
}

/// Hardware constraints
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum HardwareConstraint {
    ConnectivityGraph,
    GateTimes,
    ErrorRates,
    CoherenceTimes,
    CouplingStrengths,
}

/// Resource constraints
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum ResourceConstraint {
    QubitCount,
    CircuitDepth,
    ExecutionTime,
    MemoryUsage,
    PowerConsumption,
}

/// Performance constraints
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum PerformanceConstraint {
    LogicalErrorRate,
    ThroughputTarget,
    LatencyBound,
    FidelityThreshold,
    SuccessRate,
}

// Additional helper structs for config compatibility

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeedbackControlConfig {
    pub enable_feedback: bool,
    pub control_loop_frequency: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LearningConfig {
    pub algorithms: Vec<String>,
    pub hyperparameters: std::collections::HashMap<String, f64>,
}

// Default implementations for helper configs

impl Default for FeedbackControlConfig {
    fn default() -> Self {
        Self {
            enable_feedback: true,
            control_loop_frequency: Duration::from_millis(100),
        }
    }
}

impl Default for LearningConfig {
    fn default() -> Self {
        Self {
            algorithms: vec!["gradient_descent".to_string()],
            hyperparameters: std::collections::HashMap::new(),
        }
    }
}

// Additional configuration types for QEC compatibility

/// Pattern recognition configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternRecognitionConfig {
    pub enable_recognition: bool,
    pub recognition_methods: Vec<String>,
    pub confidence_threshold: f64,
    pub ml_model_path: Option<String>,
}

/// Statistical analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalAnalysisConfig {
    pub enable_statistics: bool,
    pub analysis_methods: Vec<String>,
    pub statistical_tests: Vec<String>,
    pub significance_level: f64,
}

/// Noise scaling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NoiseScalingConfig {
    pub scaling_factors: Vec<f64>,
    pub scaling_methods: Vec<String>,
    pub max_scaling: f64,
}

// Default implementations for new config types

impl Default for PatternRecognitionConfig {
    fn default() -> Self {
        Self {
            enable_recognition: true,
            recognition_methods: vec!["neural_network".to_string()],
            confidence_threshold: 0.9,
            ml_model_path: None,
        }
    }
}

impl Default for StatisticalAnalysisConfig {
    fn default() -> Self {
        Self {
            enable_statistics: true,
            analysis_methods: vec!["correlation".to_string(), "trend_analysis".to_string()],
            statistical_tests: vec!["chi_square".to_string()],
            significance_level: 0.05,
        }
    }
}

impl Default for NoiseScalingConfig {
    fn default() -> Self {
        Self {
            scaling_factors: vec![1.0, 1.5, 2.0, 2.5, 3.0],
            scaling_methods: vec!["folding".to_string()],
            max_scaling: 5.0,
        }
    }
}

// Simplified helper functions for creating basic ML configurations
fn create_stub_ml_optimization_config() -> crate::ml_optimization::MLOptimizationConfig {
    // Create a minimal configuration using default implementations
    crate::ml_optimization::MLOptimizationConfig {
        enable_optimization: true,
        model_config: crate::ml_optimization::MLModelConfig {
            primary_algorithms: vec![crate::ml_optimization::MLAlgorithm::DeepNeuralNetwork],
            fallback_algorithms: vec![crate::ml_optimization::MLAlgorithm::RandomForest],
            hyperparameters: std::collections::HashMap::new(),
            training_config: crate::ml_optimization::TrainingConfig {
                max_iterations: 100,
                learning_rate: 0.001,
                batch_size: 32,
                early_stopping: crate::ml_optimization::EarlyStoppingConfig {
                    enable_early_stopping: false,
                    patience: 10,
                    min_improvement: 0.001,
                    restore_best_weights: true,
                },
                cv_folds: 5,
                train_test_split: 0.8,
                optimizer: crate::ml_optimization::TrainingOptimizer::Adam,
            },
            model_selection: crate::ml_optimization::ModelSelectionStrategy::CrossValidation,
            regularization: crate::ml_optimization::RegularizationConfig {
                l1_lambda: 0.0,
                l2_lambda: 0.01,
                dropout_rate: 0.0,
                batch_normalization: false,
                weight_decay: 0.0,
            },
        },
        feature_extraction: create_stub_feature_extraction_config(),
        hardware_prediction: create_stub_hardware_prediction_config(),
        online_learning: create_stub_online_learning_config(),
        transfer_learning: create_stub_transfer_learning_config(),
        ensemble_config: create_stub_ensemble_config(),
        optimization_strategy: create_stub_optimization_strategy_config(),
        validation_config: crate::ml_optimization::validation::MLValidationConfig::default(),
        monitoring_config: create_stub_ml_monitoring_config(),
    }
}

fn create_stub_feature_extraction_config() -> crate::ml_optimization::FeatureExtractionConfig {
    crate::ml_optimization::FeatureExtractionConfig {
        enable_syndrome_history: false,
        history_length: 5,
        enable_spatial_features: false,
        enable_temporal_features: false,
        enable_correlation_features: false,
        enable_auto_extraction: false,
        circuit_features: crate::ml_optimization::features::CircuitFeatureConfig {
            basic_properties: false,
            gate_distributions: false,
            depth_analysis: false,
            connectivity_patterns: false,
            entanglement_measures: false,
            symmetry_analysis: false,
            critical_path_analysis: false,
        },
        hardware_features: crate::ml_optimization::features::HardwareFeatureConfig {
            topology_features: false,
            calibration_features: false,
            error_rate_features: false,
            timing_features: false,
            resource_features: false,
            environmental_features: false,
        },
        temporal_features: crate::ml_optimization::features::TemporalFeatureConfig {
            time_series_analysis: false,
            trend_detection: false,
            seasonality_analysis: false,
            autocorrelation_features: false,
            fourier_features: false,
        },
        statistical_features: crate::ml_optimization::features::StatisticalFeatureConfig {
            moment_features: false,
            distribution_fitting: false,
            correlation_features: false,
            outlier_features: false,
            normality_tests: false,
        },
        graph_features: crate::ml_optimization::features::GraphFeatureConfig {
            centrality_measures: false,
            community_features: false,
            spectral_features: false,
            path_features: false,
            clustering_features: false,
        },
        feature_selection: crate::ml_optimization::features::FeatureSelectionConfig {
            enable_selection: false,
            selection_methods: vec![
                crate::ml_optimization::features::FeatureSelectionMethod::VarianceThreshold,
            ],
            num_features: None,
            selection_threshold: 0.05,
        },
        dimensionality_reduction: crate::ml_optimization::features::DimensionalityReductionConfig {
            enable_reduction: false,
            reduction_methods: vec![],
            target_dimensions: None,
            variance_threshold: 0.95,
        },
    }
}

const fn create_stub_hardware_prediction_config() -> crate::ml_optimization::HardwarePredictionConfig
{
    crate::ml_optimization::HardwarePredictionConfig {
        enable_prediction: false,
        prediction_targets: vec![],
        prediction_horizon: std::time::Duration::from_secs(300),
        uncertainty_quantification: false,
        multi_step_prediction: false,
        hardware_adaptation: crate::ml_optimization::hardware::HardwareAdaptationConfig {
            enable_adaptation: false,
            adaptation_frequency: std::time::Duration::from_secs(3600),
            adaptation_triggers: vec![],
            learning_rate_adaptation: false,
        },
    }
}

const fn create_stub_online_learning_config() -> crate::ml_optimization::OnlineLearningConfig {
    crate::ml_optimization::OnlineLearningConfig {
        enable_online_learning: false,
        learning_rate_schedule:
            crate::ml_optimization::online_learning::LearningRateSchedule::Constant,
        memory_management: crate::ml_optimization::online_learning::MemoryManagementConfig {
            max_buffer_size: 1000,
            eviction_strategy: crate::ml_optimization::online_learning::MemoryEvictionStrategy::LRU,
            replay_buffer: false,
            experience_prioritization: false,
        },
        forgetting_prevention:
            crate::ml_optimization::online_learning::ForgettingPreventionConfig {
                elastic_weight_consolidation: false,
                progressive_networks: false,
                memory_replay: false,
                regularization_strength: 0.0,
            },
        incremental_learning: crate::ml_optimization::online_learning::IncrementalLearningConfig {
            incremental_batch_size: 32,
            update_frequency: std::time::Duration::from_secs(300),
            stability_plasticity_balance: 0.5,
            knowledge_distillation: false,
        },
    }
}

const fn create_stub_transfer_learning_config() -> crate::ml_optimization::TransferLearningConfig {
    crate::ml_optimization::TransferLearningConfig {
        enable_transfer_learning: false,
        source_domains: vec![],
        transfer_methods: vec![],
        domain_adaptation: crate::ml_optimization::DomainAdaptationConfig {
            enable_adaptation: false,
            adaptation_methods: vec![],
            similarity_threshold: 0.5,
            max_domain_gap: 1.0,
        },
        meta_learning: crate::ml_optimization::MetaLearningConfig {
            enable_meta_learning: false,
            meta_algorithms: vec![],
            inner_loop_iterations: 1,
            outer_loop_iterations: 1,
        },
    }
}

const fn create_stub_ensemble_config() -> crate::ml_optimization::EnsembleConfig {
    crate::ml_optimization::EnsembleConfig {
        enable_ensemble: false,
        ensemble_methods: vec![],
        num_models: 1,
        voting_strategy: crate::ml_optimization::VotingStrategy::Majority,
        diversity_measures: vec![],
        dynamic_selection: false,
    }
}

fn create_stub_optimization_strategy_config() -> crate::ml_optimization::OptimizationStrategyConfig
{
    crate::ml_optimization::OptimizationStrategyConfig {
        constraint_handling: crate::ml_optimization::optimization::ConstraintHandlingConfig {
            constraint_types: vec![crate::ml_optimization::optimization::ConstraintType::Box],
            penalty_methods: vec![
                crate::ml_optimization::optimization::PenaltyMethod::ExteriorPenalty,
            ],
            constraint_tolerance: 0.1,
            feasibility_preservation: false,
        },
        search_strategies: vec![],
        exploration_exploitation: crate::ml_optimization::ExplorationExploitationConfig {
            initial_exploration_rate: 0.1,
            exploration_decay: 0.95,
            min_exploration_rate: 0.01,
            exploitation_threshold: 0.9,
            adaptive_balancing: false,
        },
        adaptive_strategies: crate::ml_optimization::AdaptiveStrategyConfig {
            enable_adaptive: false,
            strategy_selection: vec![],
            performance_feedback: false,
            strategy_mutation: false,
        },
        multi_objective: crate::ml_optimization::MultiObjectiveConfig {
            enable_multi_objective: false,
            objectives: std::collections::HashMap::new(),
            pareto_optimization: false,
            scalarization_methods: vec![],
        },
    }
}

fn create_stub_ml_monitoring_config() -> crate::ml_optimization::MLMonitoringConfig {
    crate::ml_optimization::MLMonitoringConfig {
        enable_real_time_monitoring: false,
        performance_tracking: false,
        drift_detection: crate::ml_optimization::DriftDetectionConfig {
            enable_detection: false,
            detection_methods: vec![],
            significance_threshold: 0.05,
            window_size: 100,
        },
        anomaly_detection: false,
        alert_thresholds: std::collections::HashMap::new(),
    }
}

fn create_default_validation_config() -> crate::ml_optimization::validation::MLValidationConfig {
    crate::ml_optimization::validation::MLValidationConfig::default()
}